Cross-Attention for AES Mode Variation in Side-Channel Analysis

Portability poses a significant challenge for Deep Learning (DL)-based profiling Side-Channel Analysis (SCA) on AES encryption, as attackers cannot always ensure that training and target samples use the same encryption mode. To address this, we propose an Unsupervised Domain Adaptation (UDA) DL-SCA framework for achieving effective and robust cross-encryption-mode attacks. By incorporating cross-attention and UDA techniques, our framework aligns high-dimensional input samples, reducing interference from encryption mode mismatches. Evaluation across five distinct AES modes demonstrates that our method achieves robust SCA performance without requiring prior knowledge or multiple labeled datasets for analysis.